Reduced Order Modeling of Compressible Flows with Unsteady Normal Shock Motion

Marley, Christopher D.; Duraisamy, Karthik; Driscoll, James F.

doi:10.2514/6.2015-3988

Cited by 3 publications

(2 citation statements)

References 20 publications

(18 reference statements)

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“…We will pursue projection-based model reduction techniques 24,25 as these techniques have shown promise in describing nonlinear dynamics with moving shocks. [26][27][28] An introduction to POD/Galerkin method to fluid flows can be found in Ref. 24.…”

Section: Projection-based Reduced-order Modelingmentioning

confidence: 99%

Reduced-Order Modeling of Model Rocket Combustors

Duraisamy

2017

53rd AIAA/SAE/ASEE Joint Propulsion Conference

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This work reports preliminary results on the application of projection-based reducedorder models (ROMs) to the study of combustion dynamics in liquid-fueled rocket engines. Specifically, quasi-one-dimensional and axisymmetric (two-dimensional) models of a single element rocket combustor are explored. In the one-dimensional case, a POD-Galerkinbased ROM is shown to accurately reproduce the evolution of pressure perturbations over a wide region in parameter space. Next, the viability of sampling/interpolation techniques in accelerating ROM solutions is explored. Three techniques-the discrete empirical interpolation method (DEIM), a QR decomposition-based DEIM (QDEIM) and an adaptive technique (ADEIM) are used to reconstruct the solution in the entire domain based on sparse samples. All three techniques are shown to yield a low reconstruction error based on a very small fraction of sampling points (< 1% for the axisymmetric case). The ADEIM, by virtue of dynamically adapting the basis functions and sampling points, is seen to reduce the reconstruction error considerably in comparison to the DEIM/QDEIM, suggesting its viability in predictive ROMs of realistic rocket combustors.

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Section: Projection-based Reduced-order Modelingmentioning

confidence: 99%

Reduced-Order Modeling of Model Rocket Combustors

Duraisamy

2017

53rd AIAA/SAE/ASEE Joint Propulsion Conference

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“…Recently there have been a number of papers on reduced order modelling of compressible fluids, e.g. with shock waves [2,53,54,55,56,57,58,59]. Most of existing ROMs for shock waves use the Galerkin (or Petrov-Galerkin) projection and POD approaches to generate the reduced order models.…”

Section: Model Reductionmentioning

confidence: 99%

A non-intrusive reduced-order model for compressible fluid and fractured solid coupling and its application to blasting

Xiao¹,

Yang²,

Fang

et al. 2017

Journal of Computational Physics

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This work presents the first application of a non-intrusive reduced order method to model solid interacting with compressible fluid flows to simulate crack initiation and propagation. In the high fidelity model, the coupling process is achieved by introducing a source term into the momentum equation, which represents the effects of forces of the solid on the fluid. A combined single and smeared crack model with the MohrCoulomb failure criterion is used to simulate crack initiation and propagation. The non-intrusive reduced order method is then applied to compressible fluid and fractured solid coupled modelling where the computational cost involved in the full high fidelity simulation is high. The non-intrusive reduced order model (NIROM) developed here is constructed through proper orthogonal decomposition (POD) and a radial basis function (RBF) multi-dimensional interpolation method.The performance of the NIROM for solid interacting with compressible fluid flows, in the presence of fracture models, is illustrated by two complex test cases: an immersed wall in a fluid and a blasting test case. The numerical simulation results show that the NIROM is capable of capturing the details of compressible fluids and fractured solids while the CPU time is reduced by several orders of magnitude. In addition, the issue of whether or not to subtract the mean from the snapshots before applying POD is discussed in this paper. It is shown that solutions of the NIROM, without mean subtracted before constructing the POD basis, captured more details than the NIROM with mean subtracted from snapshots.

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